Abstract: Embodiments discussed herein facilitate determining a prognosis for keratoplasty based on segmented endothelial cells. One example embodiment comprises a computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: accessing an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty; segmenting, based at least in part on a first model, a plurality of corneal endothelial cells of the set of corneal endothelial cells; calculating one or more features based on the segmented plurality of corneal endothelial cells; and generating, via a second model trained based at least on the one or more features, a prognosis associated with the keratoplasty.

Abstract: Systems, methods, and apparatus are provided for determining a risk prediction for major adverse cardiovascular event (MACE) for a patient based on a computed tomography (CT) calcium score image of the patient's chest. In one example, a method includes receiving a computed tomography (CT) calcium score image of a chest; identifying tissue of interest in the CT calcium score image; analyzing the CT calcium score image to determine features of the identified tissue of interest; and determining a risk prediction of MACE based on the features.

Abstract: Embodiments discussed herein facilitate automated and/or semi-automated segmentation of endothelial cells via a trained deep learning model. One example embodiment is a method, comprising: accessing an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty; pre-processing the optical microscopy image to generate a pre-processed optical microscopy image via correcting for at least one of shading or illumination artifacts in the optical microscopy image; segmenting, based at least in part on a trained deep learning (DL) model, a plurality of corneal endothelial cells of the set of corneal endothelial cells in the pre-processed optical microscopy image; and displaying, via a graphical user interface (GUI), at least the segmented plurality of corneal endothelial cells.

Abstract: A first set of embodiments relates to an apparatus comprising one or more processors configured to: access three-dimensional (3D) ultrasound imaging of an eye; generate at least one segmented ocular structure by segmenting at least one ocular structure represented in the 3D ultrasound imaging using at least one deep learning ocular structure segmentation model configured to generate a predicted segmentation volume of the at least one ocular structure based on at least one portion of the 3D ultrasound imaging; compute at least one clinical metric associated with the at least one segmented ocular structure based on the at least one segmented ocular structure; and display at least one of: the at least one segmented ocular structure, the at least one clinical metric, the 3D ultrasound imaging, or at least one portion of the 3D ultrasound imaging.

Abstract: The present disclosure, in some embodiments, relates to a method of determining a stent effectiveness. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient. One or more pre-stent label volumes of the blood vessel are determined and one or more treatment variables associated with the pre-stent intravascular image are determined. One or more FEM-mimic simulations are generated by applying a first deep learning model to the one or more pre-stent label volumes and the one or more treatment variables. The one or more FEM-mimic simulations are used to determine a stent effectiveness metric.

Abstract: The present disclosure, in some embodiments, relates to a method of generating a prognosis for a patient. The method includes accessing automatically segmented pericoronary adipose tissue (PCAT) corresponding to a patient within an electronic memory. A plurality of non-confounding PCAT features are generated by measuring values of Hounsfield units for an imaging unit within the PCAT. The measured values of the Hounsfield units are predominately free of iodine confounding and artifacts. The plurality of non-confounding PCAT features are provided to a regression model.

Abstract: Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Abstract: The present disclosure, in some embodiments, relates to a method of predicting stent expansion. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient and segmenting the pre-stent intravascular image to identify a lumen and a calcification lesion. A plurality of features are extracted from one or more of the lumen and the calcification lesion. A regression model is applied to one or more of the plurality of features to determine a minimum stent expansion metric (mSEM). The mSEM indicating how much a stent will expand after implantation. The mSEM is used to generate a classification of the blood vessel as an under-expanded area or a well-expanded area.

Abstract: A device (12, 312, 494) operates to cause financial transfers responsive to data read from data bearing records. The device includes a reader (20, 314) that is usable to read check data from financial checks. The reader is also usable to read record document data such as an invoice or other remittance advice. At least one circuit (54, 332) of the device is operative to cause a determination to be made that check data and/or record document data corresponds to stored data. Responsive to the determination, check data and record data are made available to a payee terminal (346). The device may be operable responsive to receipt of embedded instructions from a check payment website to capture image data corresponding to an image of a financial check and to send the image data to a remote computer as indicated by the embedded instructions.

Abstract: An adaptive torque disturbance cancellation method and motor control system for rotating a load are described. The system has: (i) a speed controller for receiving a first input signal indicating a desired motor speed and, in response, for outputting a motor control signal; (ii) current sensing circuitry for sensing current through a motor that rotates in response to the speed controller; (iii) circuitry for storing, into a storage device, history data representative of the current through a motor when the motor operates to rotate the load; and (iv) circuitry for modifying the motor control signal in response to the history data.

Abstract: Embodiments discussed herein facilitate system-independent quantitative perfusion measurements. One example embodiment is a method, comprising: accessing 4D (Four Dimensional) perfusion imaging data of a tissue, where the 4D perfusion imaging data comprises a plurality of 3D (Three Dimensional) stacks of perfusion imaging data over time; performing at least one of artifact reduction or post-processing on the 4D perfusion image data to generate processed 4D perfusion image data; computing one or more quantitative perfusion parameters for the tissue based at least in part on the processed 4D perfusion image data; and outputting a visual representation of the one or more quantitative perfusion parameters.

Abstract: Embodiments discussed herein facilitate employing a pretrained model to determine risk(s) of adverse event(s) based on Computed Tomography (CT) image volume(s) of an artery and/or training a model to determine such risk(s). Example embodiments can determine risk based on territory-specific calcium score(s) and/or intensity/morphological/location features discussed herein. Various embodiments can determine risk(s) based on a single CT image volume and/or change(s) in CT image volumes taken over a series of time points.

Abstract: A device (12, 312, 494) operates to cause financial transfers responsive to data read from data bearing records. The device includes a reader (20, 314) that is usable to read check data from financial checks. The reader is also usable to read record document data such as an invoice or other remittance advice. At least one circuit (54, 332) of the device is operative to cause a determination to be made that check data and/or record document data corresponds to stored data. Responsive to the determination, check data and record data are made available to a payee terminal (346). The device may be operable responsive to receipt of embedded instructions from a check payment website to capture image data corresponding to an image of a financial check and to send the image data to a remote computer as indicated by the embedded instructions.

Abstract: A first set of embodiments relates to an apparatus comprising one or more processors configured to: access three-dimensional (3D) ultrasound imaging of an eye; generate at least one segmented ocular structure by segmenting at least one ocular structure represented in the 3D ultrasound imaging using at least one deep learning ocular structure segmentation model configured to generate a predicted segmentation volume of the at least one ocular structure based on at least one portion of the 3D ultrasound imaging; compute at least one clinical metric associated with the at least one segmented ocular structure based on the at least one segmented ocular structure; and display at least one of: the at least one segmented ocular structure, the at least one clinical metric, the 3D ultrasound imaging, or at least one portion of the 3D ultrasound imaging.

Abstract: Embodiments discussed herein facilitate classification of vascular plaque. One example embodiment can: access vascular imaging data comprising one or more slices, wherein each slice comprises a plurality of A-lines of that slice; for each A-line of the plurality of A-lines of each slice of the one or more slices: extract one or more features for that A-line, wherein the one or more features for that A-line comprise at least one of: one or more features extracted from that A-line, one or more features extracted from the slice comprising that A-line, or one or more features extracted from the vascular imaging data; provide the one or more features for that A-line to at least one classifier; and generate a classification of that A-line via the at least one classifier, wherein the classification of that A-line is one of fibrocalcific, fibrolipidic, or other.

Abstract: Embodiments discussed herein facilitate automated and/or semi-automated segmentation of endothelial cells via a trained deep learning model. One example embodiment is a method, comprising: accessing an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty; pre-processing the optical microscopy image to generate a pre-processed optical microscopy image via correcting for at least one of shading or illumination artifacts in the optical microscopy image; segmenting, based at least in part on a trained deep learning (DL) model, a plurality of corneal endothelial cells of the set of corneal endothelial cells in the pre-processed optical microscopy image; and displaying, via a graphical user interface (GUI), at least the segmented plurality of corneal endothelial cells.

Abstract: Probes, methods and kits for detecting and measuring abasic (AP) sites in a nucleic acid are provided. Aspects of the methods include determining glycosylase enzyme activity. Further provided herein are methods of quantifying AP sites in genomic DNA, and quantifying the amount of DNA damage. The subject probes include a fluorophore linked to an alpha nucleophile that reacts with the AP site of the nucleic acid to produce a highly fluorescent conjugate.

Abstract: Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Abstract: Embodiments discussed herein facilitate determining a prognosis for keratoplasty based on segmented endothelial cells. One example embodiment comprises a computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: accessing an optical microscopy image comprising a set of corneal endothelial cells of a patient of a keratoplasty; segmenting, based at least in part on a first model, a plurality of corneal endothelial cells of the set of corneal endothelial cells; calculating one or more features based on the segmented plurality of corneal endothelial cells; and generating, via a second model trained based at least on the one or more features, a prognosis associated with the keratoplasty.

Abstract: A device (12, 312, 494) operates to cause financial transfers responsive to data read from data bearing records. The device includes a reader (20, 314) that is usable to read check data from financial checks. The reader is also usable to read record document data associated with goods provided to a purchaser. At least one circuit (54, 332) of the device is operative to cause a determination to be made that check data and/or record document data corresponds to stored data. Responsive to the determination, check data and record data are made available to a payee terminal (346).